Ponding water detection on satellite imagery

ABSTRACT

A system for identifying ponding water located on a field from image data is described. In an approach, an image of an agricultural field is analyzed using a classifier that has been trained based on the spectral bands of labeled image pixels to identify a probability for each pixel within the image that the pixel corresponds to water. A flow simulation is performed to determine regions of the field that are likely to pool water after rainfall based on precipitation data, elevation data, and soil property data of the field. A graph of vertices representing the pixels and edges representing connections between neighboring pixels is generated. The probability of each pixel within the graph being ponding water is set based on the probability pixel being water, the likelihood that water will pool in the area represented by the pixel, the probability of neighboring pixels being ponding water, and a cropland mask that identifies which pixels correspond to cropland. A class for each pixel is then determined that maximizes the joint probability over the graph.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as aContinuation of application Ser. No. 14/860,247, filed Sep. 21, 2015,the entire contents of which is hereby incorporated by reference for allpurposes as if fully set forth herein. The applicants hereby rescind anydisclaimer of claim scope in the parent applications or the prosecutionhistory thereof and advise the USPTO that the claims in this applicationmay be broader than any claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. ©2015 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to detecting ponding water withinagricultural fields using programmed computer systems to analyze digitalimages. More specifically, the present disclosure relates to detectingponding water within agricultural fields by analyzing satellite imageryof said agricultural fields.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Ponding water (also referred to as standing water, stagnated water, orpooled water) is typically an unwanted body of water that temporarilypools on a location, such as a field or roof, after rainfall. Forexample, after heavy rain, certain areas of a farmer's field based ongeographical properties such as elevation and properties of the soil mayexperience a pooling of water. Although ponding water typicallydissipates after some time, either through evaporation or absorptioninto the soil, in the meantime ponding water can be extremelydetrimental to the growth of certain crops. For example, the pondingwater may be pooled on top of the crops and cause the crops to becomeoverwatered. Newly emerged seedlings, or crops such as turf grass thatare short even at maturity, can become inundated and oxygen starved,resulting in severe damage or total loss under the ponded area. This isespecially dangerous during early growing seasons when overwatering cancause a drastic decrease in crop yield come harvest.

In order to detect ponding water, farmers have typically had tophysically traverse the fields after rainfall to determine if anyponding water has formed that could potentially harm the crops. If suchdangerous accumulations of water are located, the farmers drain the areato keep the crops healthy and prevent losses in crop yield. However,modern farms can span extremely large areas and it may not be practicalto physically visit all the potential areas where ponding water mightappear. As a result, a field of research has grown around techniques toautomatically detect ponding water from multispectral images captured bysatellite. These spectral approaches typically rely on the fact thatwater strongly absorbs incoming radiation in the near to mid-infraredwavelengths. Thus, rather than the farmer physically going out toinspect the fields, satellite imagery of the agricultural fields can betaken on a periodic basis and analyzed to determine areas where standingwater has accumulated.

Many standing water detection techniques have been developed in recentyears. These techniques include simple thresholding using infraredbands, combinations of two or more bands or indices, such as thenormalized difference vegetation index (NDVI) and the normalizeddifference water index (NDWI), and the inclusion of auxiliary variablesrelated to relief and topography for water detection purpose. Existingtechniques tend to rely on large-scale water detection, such aspermanent water or flooding events with Landsat images. However, suchtechniques translate poorly to detecting within-field ponding water.

There are many challenges to differentiating water signals from otherland cover signals in remotely sensed data, such as satellite imagerydata. Water quality and depth affect the spectral response particularlyin the visible portion of the spectrum. In fact, a great deal ofresearch has been devoted to techniques for monitoring water quality andbathymetry using remote sensing data. Other factors which createchallenges for mapping standing water are related to the mixed spectralresponse of pixels covered partially by water (also called mixed pixelsor “mixels”). Under certain resolutions, such as in 5 m resolutionimages, the pixels can be mixed of multiple end-members such as soil,residue, vegetation, water as in furrow irrigation or in floodedwetlands where vegetation is not completely submerged. Another issuecomes from the fact that most high-resolution remote sensing imagery isnot atmospherically corrected, so that the water content or aerosoldepth impacts the sensed signals. Given these issues, within-fieldponding water detection becomes an extremely challenging problem.

SUMMARY OF THE DISCLOSURE

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more externaldata sources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 illustrates a process for generating a water probability map fora satellite image in block diagram form according to an embodiment.

FIG. 6 illustrates a process for generating a potential ponding map fora field in block diagram form according to an embodiment.

FIG. 7 illustrates a process for generating a per-pixel ponding waterclassification of a satellite image in block diagram form according toan embodiment.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. The description is provided according to the followingoutline:

-   -   1.0 General Overview    -   2.0 Example Agricultural Intelligence Computer System        -   2.1 Structural Overview        -   2.2 Application Program Overview        -   2.3 Data Ingest to the Computer System        -   2.4 Process Overview—Agronomic Model Training        -   2.5 Ponding Water Detection Subsystem        -   2.6 Implementation Example—Hardware Overview    -   3.0 Example System Inputs        -   3.1 Remote Sensing Data        -   3.2 Precipitation Data        -   3.3 Elevation Data        -   3.4 Soil Property Data        -   3.5 Cropland Data    -   4.0 Analysis Triggers    -   5.0 Spectral Analysis        -   5.1 Preparing Ground Truth Data        -   5.2 Training and Using the Classifier    -   6.0 Flow Simulation    -   7.0 Coupling Analysis    -   8.0 Alert Examples        -   8.1 Guided Tour of Detected Problem Areas        -   8.2 Weather Index Insurance Applications    -   9.0 Alternative Applications    -   10.0 Extensions and Alternatives    -   11.0 Additional Disclosure

1.0 GENERAL OVERVIEW

Aspects of this disclosure focus on the problem of detecting standingwater within agricultural fields, particularly in early growing seasonswhen crops are more sensitive to being overwatered. In onecomputer-implemented approach, a programmed model is developed fordetecting standing water, the input of which is electronically storeddigital imagery taken of the agricultural field and digital datarepresenting properties of the field such as elevation data,precipitation data, and soil property data. In some cases, a digitalcropland mask, which identifies which pixels of the imagery correspondto agricultural fields/cropland, is used as a filter during theanalysis.

In an embodiment, a model for detecting standing water is supplied withinputs related to the agricultural field(s) such as satellite imagedata, precipitation data, elevation data, soil property, and a croplandfield mask. Conceptually, the model can be divided into four componentswhich represent the logic employed by the computing system to detectponding water. However, the techniques described herein may beimplemented in embodiments which organize functionally equivalent logicin ways other than the exact four components discussed.

The spectral analysis logic analyzes spectral bands from a satelliteimage of the field and provides a water probability map. The waterprobability map indicates, for individual pixels of the satellite image,what the probability is that the pixel represents water. In someembodiments, the spectral analysis logic implements a classifier thathas been trained on labeled imagery to generate the probability. Forexample, the spectral analysis logic may employ a logistic regressionmodel that uses the spectral bands of each pixel as features.

The flow simulation logic uses the measured level of precipitation fromthe previous rainfall, along with a geographic elevation map of thefield and absorption properties of the soil within the field, toestimate regions where rainwater is likely to pool. For example, aniterative algorithm can be used to estimate from a starting position ofwater from the rainfall on the field, where the water is likely to flowand in what quantities based on the elevation of the region andabsorption rate of the soil. After a number of iterations, areas in thesimulation which still contain water are marked as potential pondingareas.

The coupling logic combines the results of the spectral analysis logicwith the results of the flow simulation logic. The coupling logic worksunder the assumption that a pixel which accurately represents water hasa greater likelihood to be surrounded by other water pixels, rather thanby pixels representing dry land. As a result, a pixel which has a highprobability of being a water pixel also increases the likelihood thatneighboring pixels also represent water. In addition, the model assumesthat pixels with a high probability of being water (as determined by thespectral analysis logic) and are in an area in which water is likely topool (as determined by the flow simulation logic) has a higherprobability of representing ponding water.

The coupling logic formalizes the aforementioned concepts into aconcrete workable model. For example, the satellite image could beviewed as a graph, where each node represents a pixel and is connectedto the neighboring pixels with an edge. The probability that each pixelrepresents water is based on a weighted combination of value of thewater probability map for the pixel and the likelihood of water poolingin the area represented by the pixel based on the flow simulation. Forexample, if the pixel has both a high probability of being water and isin a position where flooding is likely to occur, the probability of thatpixel being ponding water is strengthened. However, if the twosub-models disagree (or agree the pixel represents dry-land), theprobability of the pixel representing ponding water is decreasedaccordingly.

Furthermore, the probability of a pixel being ponding water is notconsidered in a vacuum, instead the probability of the neighboringpixels being ponding water is also taken into consideration. Forexample, bolstering the probability of representing ponding water if theneighbors are likely to be water pixels and weakening the probability ifthe neighbors are likely to be dry-land pixels. Thus, the modelestimates the class (water or dry-land) for each pixel that maximizesthe joint probability over the graph. In some embodiments, the couplinglogic utilizes a Markov Random Field (MRF) to model the coupling betweenthe pixels. Furthermore, in some embodiments, the goal is to detectponding water only on agricultural fields/cropland. As a result, acropland field mask is utilized to filter out pixels which do notrepresent cropland, effectively preventing non-cropland pixels frombeing classified as ponding water.

The alert logic uses the result of the coupling logic (the per-pixelclassification of water or dry-land) to generate an alert (e.g. emails,text messages, and so forth) that is sent to a device of the farmer orother agricultural agent that warns of potentially dangerous pondingwater on the fields and the specific location(s) to investigate. In someembodiments, in addition to the locations, the alert logic also computesan optimal route for the farmer to take which visits all the discoveredproblem areas in the field(s).

Thus, the techniques described herein provide a benefit in thattechniques pin-point the areas in the agricultural fields likely tocontain ponding water so that the farmer can more efficiently locate anddrain the areas of ponding water. Since ponding water can drasticallydecrease crop yield, the sooner the farmer can locate and drain theponding water, the greater chance that crop yield can be preserved evenin cases of severe flooding.

Many of the examples presented herein assume that the satellite imageryis capable of detecting various bands, such as the blue, green, red, rededge, and near infrared (NIR) bands at various resolutions. As aconcrete example, the satellite imagery used as input to the model maybe RAPIDEYE satellite image data, commercially available from a varietyof satellite service providers, such as the Satellite ImagingCorporation headquartered in Tomball, Tex. However, the techniquesdescribed herein are not limited to any particular type of imagery. Forexample, some embodiments may use imagery taken from a plane flying overthe field, rather than satellite imagery.

Other features and aspect of the disclosure will become apparent in thedrawings, description, and claims.

2.0 EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates, or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computing device 104 is programmed or configured to providefield data 106 to an agricultural intelligence computer system 130 viaone or more networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source), (f)pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant), (g) irrigation data (for example,application date, amount, source), (h) weather data (for example,precipitation, temperature, wind, forecast, pressure, visibility,clouds, heat index, dew point, humidity, snow depth, air quality,sunrise, sunset), (i) imagery data (for example, imagery and lightspectrum information from an agricultural apparatus sensor, camera,computer, smartphone, tablet, unmanned aerial vehicle, planes orsatellite), (j) scouting observations (photos, videos, free form notes,voice recordings, voice transcriptions, weather conditions (temperature,precipitation (current and over time), soil moisture, crop growth stage,wind velocity, relative humidity, dew point, black layer)), and (k)soil, seed, crop phenology, pest and disease reporting, and predictionssources and databases.

An external data server computer 108 is communicatively coupled toagricultural intelligence computer system 130 and is programmed orconfigured to send external data 110 to agricultural intelligencecomputer system 130 via the network(s) 109. The external data servercomputer 108 may be owned or operated by the same legal person or entityas the agricultural intelligence computer system 130, or by a differentperson or entity such as a government agency, non-governmentalorganization (NGO), and/or a private data service provider. Examples ofexternal data include weather data, imagery data, soil data, orstatistical data relating to crop yields, among others. External data110 may consist of the same type of information as field data 106. Insome embodiments, the external data 110 is provided by an external dataserver 108 owned by the same entity that owns and/or operates theagricultural intelligence computer system 130. For example, theagricultural intelligence computer system 130 may include a data serverfocused exclusively on a type of that might otherwise be obtained fromthird party sources, such as weather data.

An agricultural apparatus 111 has one or more remote sensors 112 fixedthereon, which sensors are communicatively coupled either directly orindirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is an example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a colorgraphical screen display that is mounted within an operator's cab of theapparatus 111. Cab computer 115 may implement some or all of theoperations and functions that are described further herein for themobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP, CANprotocol and higher-layer protocols such as HTTP, TLS, and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including to send requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user 102 may be prompted via one ormore user interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user 102 may specify identification data by accessing amap on the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user 102 mayspecify identification data by accessing field identification data(provided as shape files or in a similar format) from the U. S.Department of Agriculture Farm Service Agency or other source via theuser device and providing such field identification data to theagricultural intelligence computer system.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel data may include a model of past events on the one or more fields,a model of the current status of the one or more fields, and/or a modelof predicted events on the one or more fields. Model and field data maybe stored in data structures in memory, rows in a database table, inflat files or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system 130 independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide server-side functionality, via thenetwork 109 to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding data values representing one or more of: a geographicallocation of the one or more fields, tillage information for the one ormore fields, crops planted in the one or more fields, and soil dataextracted from the one or more fields. Field manager computing device104 may send field data 106 in response to user input from user 102specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, external APIs that push data to the mobile application, orinstructions that call APIs of external systems to pull data into themobile application.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 and programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops and to create variable rate (VR) fertility scripts.This enables growers to maximize yield or return on investment throughoptimized nitrogen application during the season. Example programmedfunctions include displaying images such as SSURGO images to enabledrawing of application zones; upload of existing grower-defined zones;providing an application graph to enable tuning nitrogen applicationsacross multiple zones; output of scripts to drive machinery; tools formass data entry and adjustment; and/or maps for data visualization,among others. “Mass data entry,” in this context, may mean entering dataonce and then applying the same data to multiple fields that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields of the same grower. For example,nitrogen instructions 210 may be programmed to accept definitions ofnitrogen planting and practices programs and to accept user inputspecifying to apply those programs across multiple fields. “Nitrogenplanting programs,” in this context, refers to a stored, named set ofdata that associates: a name, color code or other identifier, one ormore dates of application, types of material or product for each of thedates and amounts, method of application or incorporation such asinjected or knifed in, and/or amounts or rates of application for eachof the dates, crop or hybrid that is the subject of the application,among others. “Nitrogen practices programs,” in this context, refers toa stored, named set of data that associates: a practices name; aprevious crop; a tillage system; a date of primarily tillage; one ormore previous tillage systems that were used; one or more indicators ofmanure application that were used. Nitrogen instructions 210 also may beprogrammed to generate and cause displaying a nitrogen graph, once aprogram is applied to a field, which indicates projections of plant useof the specified nitrogen and whether a surplus or shortfall ispredicted; in some embodiments, different color indicators may signal amagnitude of surplus or magnitude of shortfall. In one embodiment, anitrogen graph comprises a graphical display in a computer displaydevice comprising a plurality of rows, each row associated with andidentifying a field; data specifying what crop is planted in the field,the field size, the field location, and a graphic representation of thefield perimeter; in each row, a timeline by month with graphicindicators specifying each nitrogen application and amount at pointscorrelated to month names; and numeric and/or colored indicators ofsurplus or shortfall, in which color indicates magnitude.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at external data server computer 108 and configured toanalyze metrics such as yield, hybrid, population, SSURGO, soil tests,or elevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at machine sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the NITROGEN ADVISOR, commercially available from TheClimate Corporation, San Francisco, Calif., may be operated to exportdata to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steeling.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria, controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. No. 8,767,194 and U.S. Pat. No. 8,712,148 may be used, and thepresent disclosure assumes knowledge of those patent disclosures.

2.4 Process Overview—Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge at the samelocation or an estimate of nitrogen content with a soil samplemeasurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more external datasources. FIG. 3 may serve as an algorithm or instructions forprogramming the functional elements of the agricultural intelligencecomputer system 130 to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more external data resources. The fielddata received from one or more external data resources may bepreprocessed for the purpose of removing noise and distorting effectswithin the agronomic data including measured outliers that would biasreceived field data values. Embodiments of agronomic data preprocessingmay include, but are not limited to, removing data values commonlyassociated with outlier data values, specific measured data points thatare known to unnecessarily skew other data values, data smoothingtechniques used to remove or reduce additive or multiplicative effectsfrom noise, and other filtering or data derivation techniques used toprovide clear distinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Ponding Water Detection Subsystem

In an embodiment, the agricultural intelligence computer system 130,among other components, includes a ponding water detection subsystem170. The ponding water detection subsystem 170 collects informationrelated to a field from the model data and field data repository 160and/or external data 110 and determines whether and where ponding waterhas accumulated on the field.

In an embodiment, the ponding water detection subsystem 170 includesspectral analysis logic 171, flow simulation logic 172, coupling logic173, and alert logic 174.

The spectral analysis logic 171 analyzes spectral bands from a satelliteimage of the field and provides a water probability map. The waterprobability map indicates, for individual pixels of the satellite image,what the probability is that the pixel represents water. In someembodiments, the spectral analysis logic 171 implements a classifierthat has been trained on labeled imagery to generate the probability.For example, the spectral analysis logic 171 may employ a logisticregression model that uses the spectral bands of each pixel as features.

The flow simulation logic 172 uses the measured level of precipitationfrom the previous rainfall, along with a geographic elevation map of thefield and absorption properties of the soil within the field, toestimate regions where rainwater is likely to pool. For example, aniterative algorithm can be used to estimate from a starting position ofwater from the rainfall on the field, where the water is likely to flowand in what quantities based on the elevation of the region andabsorption rate of the soil. After a number of iterations, areas in thesimulation which still contain water are marked as potential pondingareas.

The coupling logic 173 combines the results of the spectral analysislogic 171 with the results of the flow simulation logic 172. Thecoupling logic 173 works under the assumption that a pixel whichaccurately represents water has a greater likelihood to be surrounded byother water pixels, rather than by pixels representing dry land. As aresult, a pixel which has a high probability of being a water pixel alsoincreases the likelihood that neighboring pixels also represent water.In addition, the model assumes that pixels with a high probability ofbeing water (as determined by the spectral analysis logic 171) and arein an area in which water is likely to pool (as determined by the flowsimulation logic 172) has a higher probability of representing pondingwater.

The coupling logic 173 formalizes the aforementioned concepts into aconcrete workable model. For example, the satellite image could beviewed as a graph, where each node represents a pixel and is connectedto the neighboring pixels with an edge. The probability that each pixelrepresents water is based on a weighted combination of value of thewater probability map for the pixel and the likelihood of water poolingin the area represented by the pixel based on the flow simulation. Forexample, if the pixel has both a high probability of being water and isin a position where flooding is likely to occur, the probability of thatpixel being ponding water is strengthened. However, if the twosub-models disagree (or agree the pixel represents dry-land), theprobability of the pixel representing ponding water is decreasedaccordingly.

Furthermore, the probability of a pixel being ponding water is notconsidered in a vacuum, instead the probability of the neighboringpixels being ponding water is also taken into consideration. Forexample, bolstering the probability of representing ponding water if theneighbors are likely to be water pixels and weakening the probability ifthe neighbors are likely to be dry-land pixels. Thus, the modelestimates the class (water or dry-land) for each pixel that maximizesthe joint probability over the graph. In some embodiments, the couplinglogic 173 utilizes a Markov Random Field (MRF) to model the couplingbetween the pixels. Furthermore, in some embodiments, the goal is todetect ponding water only on agricultural fields/cropland. As a result,a cropland field mask is utilized to filter out pixels which do notrepresent cropland, effectively preventing non-cropland pixels frombeing classified as ponding water.

The alert logic 174 uses the result of the coupling logic 173 (theper-pixel classification of water or dry-land) to generate an alert(e.g. emails, text messages, and so forth) that is sent to a device ofthe farmer or other agricultural agent that warns of potentiallydangerous ponding water on the fields and the specific location(s) toinvestigate. In some embodiments, in addition to the locations, thealert logic 174 also computes an optimal route for the farmer to takewhich visits all the discovered problem areas in the field(s).

2.6 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3.0 EXAMPLE SYSTEM INPUTS

The exact inputs to the ponding water detection subsystem 170 may varyin different embodiments. In order to provide concrete examples, thefollowing passages identify specific example types of data that can beused by the spectral analysis logic 171, flow simulation logic 172,and/or coupling logic 173 to estimate potential problem areas within afield where ponding water is likely to have appeared. However, thetechniques described herein are not limited to any particular type ofinputs or any particular location, services, or tools used to collectthe inputs.

3.1 Remote Sensing Data

In this disclosure, “remote sensing data” is used synonymously with“satellite imagery.” Thus, many of the examples provided herein will bedescribed using satellite imagery as the remote sensing data. However,the use of satellite imagery in the following examples does not limitthe techniques herein solely to remote sensing data that is satelliteimagery. As technology develops other types of remote sensing technologymay appear and the techniques described herein are broad enough to makeuse of any emerging remote sensing devices and/or techniques. Forexample, as an alternative to satellite imagery, the techniquesdescribed herein may also apply to images taken by aircraft or dronesflying over the field.

Many of the examples presented herein assume that the satellite imageryis capable of detecting various bands, such as the blue, green, red, rededge, and near infrared (NIR) bands at various resolutions. As aconcrete example, the satellite imagery used as input to the model maybe RAPIDEYE satellite image data, which offers multispectral images at aspatial resolution of 5 m. However, the techniques described herein arenot limited to any particular type of satellite imagery.

The techniques described herein apply equally to situations where thesatellite imagery can be captured on demand or are capturedperiodically. For example, the user 102 and/or operator of theagricultural intelligence computer system 130 may contract with aseparate company that owns the satellites used to take the images.Depending on the contract, the remote sensing data used as input to theponding water detection subsystem 170 may be updated only on a periodbasis. As a result, in some cases, there may be a delay between rainfalloccurring on the field and the image capture of the field by thesatellite. Furthermore, depending on the number and positions of thesatellites that are available, on demand images may not be possibleuntil a satellite with appropriate positioning becomes available.However, in cases where the images can be taken on demand, theagricultural intelligence computer system 130 may be configured tocommunicate with a system of the satellite imagery provider andautomatically request images in response to receiving user input via adevice of the user 102, such as the field manager computing device 104,or after detecting a threshold amount of rainfall on the field from theremote sensor 112 or a separate weather base station located near thefield.

3.2 Precipitation Data

Precipitation data represents current and/or historical rainfall of thefield being analyzed by the ponding water detection subsystem 170. Insome embodiments, the precipitation data is used by the flow simulationlogic 172 to estimate areas or regions within the field that are likelyto have collected ponding water after an instance of rainfall.

In some embodiments, the precipitation data may be stored by the modeldata and field data repository 160 based on readings taken from theremote sensor 112 or other sensors established in the general area ofthe field. However, in other embodiments, the precipitation data iscollected from an external data server computer 108 belonging to aprivate weather service, public government weather collecting service,or any other service that collects and stores precipitation data forvarious areas, including the field to be analyzed.

In some cases, the precipitation data is resampled to match the spatialresolution of the satellite images, which can make some of thecalculations described later more efficient to perform. For example, theprecipitation data when resampled to the same resolution as thesatellite image could be used to identify the amount of rainfallrepresented by each pixel of the satellite image.

3.3 Elevation Data

Elevation data represents the elevation of regions within the field. Forexample, the elevation data may take the form of a topography map or anyother map that can provide a height of elevation for regions within thefield. In some embodiments, the elevation data is used by the flowsimulation logic 172 to estimate in which regions water would likelyflow to during or after rainfall. For example, water tends to flow fromhigh-elevation areas to low-elevation areas over time. In some cases,the elevation data is resampled to match the spatial resolution of thesatellite images, which can make some of the calculations describedlater more efficient to perform. For example, the elevation data whenresampled to the same resolution as the satellite image could be used toidentify the height of the area represented by each pixel of thesatellite image.

In some embodiments, the elevation data may be stored by the model dataand field data repository 160 based on a survey of the field taken bythe farmer or a private contractor. However, in other embodiments, theelevation data is collected from an external data server computer 108belonging to a private or public service that surveys various landareas, including the field to be analyzed, and records the elevation ofregions within the field. One example format for the elevation data isdigital elevation model (DEM) raster format.

3.4 Soil Property Data

Soil property data represents the properties of the soil at variousregions within the field. As water flows, the water infiltrates the soilat a rate dependent on the properties of the soil, such as watercapacity and soil electrical conductivity. Thus, the flow simulationlogic 172 can use the properties of soil found at various regions withinthe field (or just one if the soil is the same contiguous typethroughout the field) to determine how much water is absorbed as itflows from higher elevation regions to lower elevation regions.

In some embodiments, the soil type data may be stored by the model dataand field data repository 160 based on a survey of the field taken bythe farmer or a private contractor. For example, most farmers eitherhave implicit knowledge of the type of soil used in the field or havetested the soil of the field for various purposes, such as sending thesoil for nutrient testing. Thus, the user 102 may be able to manuallyinput the soil properties or have the field manager computing device 104automatically enter the soil properties based on previously stored data.However, in other embodiments, the elevation data is collected from anexternal data server computer 108 belonging to a private or publicservice that analyzes and records soil properties for various areas,including the field under analysis. One example of external data 110that indicates soil properties for regions within agricultural fields isthe Soil Survey Geographic (SSURGO) database, which is a digitaldatabase about soil collected by the National Cooperative Soil Survey(NCSS). The aforementioned database provides soil data, such aselectrical conductivity, field capacity, and so forth.

In some embodiments, the soil type property data (if a differentresolution from the satellite image) is resampled to adhere to the sameresolution as the satellite image. As a result, it becomes moreefficient for the ponding water detection subsystem 170 to correlate thesoil properties that correspond to the field area covered by a givenpixel of the satellite data.

3.5 Cropland Data

Cropland data represents a mask identifying regions that are consideredagricultural fields/cropland. For example, the Cropland Data Layer (CDL)is a raster, geo-referenced, crop-specific land cover data layer that iscreated annually for the continental United States using moderateresolution satellite imagery and extensive agricultural ground truth.Since one of the goals is to identify field areas where ponding water islikely to have appeared, the cropland data can be used to helpaccurately identify which geographical regions correlate to theagricultural field, as opposed to other areas such as lakes, oceans,mountains, or other geographical areas depicted in the image that areexternal to the field. Since land types other than agriculturalfields/cropland can be ignored in some embodiments, the cropland datacan help minimize noise that might appear if data pertaining tonon-cropland areas were considered in the analysis. In some embodiments,the cropland data is resampled to be the same resolution as thesatellite images, thus simplifying the process of correlating whichpixels specifically pertain to cropland as identified by the croplandmask. Embodiments need not be used solely with cropland or agriculturalfields, however; for example, an embodiment could be used to detectponding water in large, valuable landscaped areas, such as golf courses.

4.0 ANALYSIS TRIGGERS

As a whole, the spectral analysis logic 171, flow simulation logic 172,coupling logic 173, and alert logic 174 together implement a coherentcomputer-executed process for analyzing data related to the field,determining whether and where ponding water is likely to have appeared,and providing alerts to a user 102 or other agent informing them of theponding water that has been detected. The following are examples ofcriteria that can be used to trigger the process.

In some embodiments, the process is triggered as a result of theagricultural intelligence computer system 130 receiving a satelliteimage of the field. In some cases, depending on the contractualarrangements with the satellite image service provider, images of thefield may only be available at periodic intervals (e.g. once a day,week, month, and so forth). As a result, some embodiments may begin theprocess of detecting ponding water as soon as a new image becomesavailable. In some cases the agricultural intelligence computer system130 may receive the image from the computer system of the satelliteimage server provider without prompted (referred to as the “push” modelof data collection). Alternatively, the agricultural intelligencecomputer system 130 may send a message asking for an updated image ifone is available (referred to as the “pull” model of data collection).

In other embodiments, the agricultural intelligence computer system 130may be configured to send a message to a computer system of thesatellite image service provider requesting that an image be taken atthe present or near-present time. Thus, as one example, the agriculturalintelligence computer system 130 may, via a user interface of the fieldmanager computing device 104, receive user input specifying to requestan updated image. In response, the agricultural intelligence computersystem 130 sends a message to the computer system of the satellite imageservice provider requesting a new image be taken. In other embodiments,the agricultural intelligence computer system 130 is configured torequest an image if particular criteria are met. For example, inresponse to receiving data indicating a threshold amount of rainfall hasoccurred (e.g. from the remote sensor 112 on the field or a weatherstation in proximity to the field), the agricultural intelligencecomputer system 130 automatically requests an updated image and beginsthe detection process.

5.0 SPECTRAL ANALYSIS

The spectral analysis logic 171 is programmed to analyze spectral bandsfrom the satellite image and provides a water probability map for thefield which indicates the probability that that a given pixel representswater.

In an embodiment, the spectral analysis logic 171 determines theprobability that a given pixel represents water by training a classifieron labeled data (also referred to as the “ground truth”) and feeding thefeatures of each pixel to the trained classifier.

Many machine learning techniques, such as classifiers and certain typesof regression, involve the estimation of a function that maps between aset of inputs (referred to as features) and a set of outputs (referredto as classes or labels). The estimation of the function, referred to as“training”, is typically performed by analyzing a “training set” offeatures and their corresponding labels. By some definitions, aclassifier outputs discrete labels whereas techniques based onregression produce continuous output values. However, for certain typesof regression, such as logistic regression which produces a probabilityof being one of potentially two outcomes, this distinction is largelymeaningless. For simplicity, the examples provided herein will refer tothe machine learning technique used by the spectral analysis logic 171as classification. However, the aforementioned terminology is notintended to exclude other machine learning techniques, such asregression.

During the analysis, an optimization is performed to find the functionthat best explains the mapping between the features and thecorresponding labels in the labeled training set. The terms “best”,“maximum”, and/or “optimum” as used herein do not necessarily refer to aglobal metric. In many cases a local maximum of the likelihood of themapping between the features and the label given the function issufficient. Different machine learning techniques perform theaforementioned optimizations in different ways. For example, naive Bayesclassifiers assume independence of the features given the class andestimate a function that explains the association between the featuresand the label. As another example, artificial neural networks model theproblem domain as systems of interconnected nodes (representing“neurons”) which send messages to one another, often with some nodesrepresenting the inputs, some nodes representing intermediary or“hidden” nodes, and some nodes representing the outputs. Thus, in suchmodels, the estimation of the function involves determining the optimalweights between the edges connecting the nodes that are most likely toexplain the mappings presented in the training set. Once a classifier istrained, a new data point of features can be fed into the classifier toobtain the predicted label for that data point. In most cases,classifiers output a set of potential labels and a confidence metric orother measure of the probability that the classification is correct. Inmost cases, the label to which the classifier assigned the highestconfidence is considered the predicated label.

In the present problem domain the features are the various spectralbands recorded for each pixel of the satellite image and the class isbinary, potentially classifying the pixel as a water pixel or dry-landpixel. The techniques described herein are not limited to any particulartype of classifier. For example, the classifier utilized by the spectralanalysis logic 171 may include support vector machines (SVMs), neuralnetworks, logistic regression, Bayesian techniques, perceptrons,decisions trees, and more without limitation. In order to provide clearexamples, the remainder of this disclosure will assume the use oflogistic regression which has shown accurate results in practice, butthe techniques are not necessarily limited to logistic regression.

The exact features to utilize for the classifier can be determined inmany different ways. In some embodiments, the features are selectedbased on domain knowledge or selected manually by testing variouscombinations of features and choosing those which appear to produce themost accurate results. For example, after significant testing, the NIRband, red band, and vegetation indices (specifically the TransformedSoil Adjusted Vegetation Index (TSAVI)) were found to be reasonablefeatures on which to train the classifier. As a result, the examplesprovided herein assume that the aforementioned features are used for theclassification. However, the techniques described herein are adaptableenough to accommodate any features that could conceivably be used as abasis for water detection, including but not limited to the blue band,green band, red band, red edge band, NIR band, TSAVI, soil electricalconductivity, elevation, compound topographic index (CTI), soil fieldwater capacity, and so forth. In some embodiments, the spectral analysislogic 171 may only use the NIR band as a feature for the classification.For example, some data in practice has shown that the NIR band might bemore indicative of water than any of the other bands. Thus, in someembodiments, the NIR band is used as the sole feature to potentiallyremove noise from the training of the classifier.

5.1 Preparing Ground Truth Data

As mentioned previously, labeled “ground truth” data is required inorder to train the classifier and develop the optimum function betweenthe features and the classifications. The labeled data set can becollected in a myriad of different ways.

One way is to collect a sample of satellite images representing typicalfields that one would encounter after rainfall has occurred. Theprecipitation data for the areas covered by those sample satelliteimages can be used to verify that rainfall has occurred shortly beforethe image was taken, increasing the likelihood that at least someponding water exists within the image to train the classifier. However,in some cases, including images with no ponding water might be used asnegative examples for training the classifier. The sample images canthen be manually labeled by experts within the field to identify whichpixels represent water and which represent dry-land. Those labels canthen be used as the “ground truth”.

However, other techniques use automatic or partially automatic methodsfor detecting water in the sample images to produce the labeled “groundtruth” data. For example, water features may be identified within thesample satellite images using the Image Band Correlation Tool (IBCT) inTNTmips software, commercially available from MicroImages, Inc, ofLincoln, Nebr. The IBCT uses the tasseled cap method and plots thecorrelations between the red and NIR bands. Using the IBCT, regionswithin the sample images that represent water can be identified based onthe pixels within those regions being at or below a particular thresholdfor NIR and red reflectance. The pixels covered by those regions arethen exported as a binary raster file for each of the sample images. Thebinary raster file then represents a ponding water mask that identifiesthe regions of water pixels. Next, the ponding water mask is refined bycomparing the mask to imagery that identifies known permanent bodies.The pixels within the mask which correlate to known bodies of water canbe removed, leaving the remaining portions of the mask as indicative ofponding water. Alternatively, the ponding water mask can be refined bylooking at satellite imagery from other time periods. If a body of waterappears in both the sample images and in the historical images of thearea, there is a strong likelihood those bodies of water are permanent,rather than temporary ponding water. As an example source for thehistorical images, the National Agricultural Imagery Program (NAIP)provides images of agricultural fields which can be used for thispurpose.

5.2 Training and Using the Classifier

FIG. 5 illustrates a process for generating a water probability map fora satellite image in block diagram form according to an embodiment. Thefollowing explanation assumes that the spectral analysis logic 171implements the process illustrated in FIG. 5. FIG. 5 illustratesspecific blocks that have been laid out in a particular order. However,in other embodiments, blocks may be added, removed, divided out, merged,or rearranged compared to FIG. 5. The techniques described herein arenot limited to the exact blocks in the exact order illustrated in FIG.5.

At block 505, the spectral analysis logic 171 receives satellite pixeldata of the field to be analyzed. In an embodiment, the spectralanalysis logic 171 retrieves the satellite image from the model data andfield data repository 160. However, in other embodiments, or if themodel data and field data repository 160 does not possess an image ofthe field, the spectral analysis logic 171 may request the satelliteimage from an external data server computer 108 of a satellite imageprovider service via the communication layer 132. Additional techniquesthat could be used to receive the satellite pixel data is describedabove in Section 3.1.

At block 510, the spectral analysis logic 171 receives labeled pixeldata to be used as a training set for the classifier. Depending on theembodiment, the spectral analysis logic 171 may retrieve the labeledpixel data from the model data and field data repository 160 or anexternal data server computer 108 of a label provider. Additionaltechniques that could be used to receive or generate the labeled pixeldata is described above in Section 4.1. In some embodiments, thespectral analysis logic 171 may train the classifier pre-emptively andstore the learned function and/or coefficients of the learned functionin the model data and the field data repository 160. In such cases, thespectral analysis logic 171 can skip block 510 and block 515 and insteadproceed to block 520.

At block 515, the spectral analysis logic 171 trains a classifier basedon the labeled pixel data. Depending on the embodiment, the classifiermay be one or more of a neural network, a SVM, a Bayesian classifier, aperceptron, logistic regression, or any other machine learningtechnique. However, in order to provide a clear explanation, an exampleis provided below that utilizes logistic regression as the classifier.The example below assumes that the features are the red band, NIR band,and vegetation index with the output probability being the probabilityof being a water pixel as opposed to a dry-land pixel.

The general logistic regression model is as follows:

$\begin{matrix}{W_{i,j} = \frac{1}{1 + e^{{- A^{T}}X}}} & \left( {{Equation}\mspace{14mu} 1.0} \right)\end{matrix}$

Where W_(i,j) is the water probability of pixel (i,j), X is the featurevector and A is the coefficient vector. Thus, the training of thelogistic regression model involves determining the coefficient vector Athat best describes the relationship between the feature vector X andthe water probability for each pixel of the images included in thelabeled pixel data. For example, the labeled training set may considerthe probability is 1 if labeled as water and 0 if labeled as dry-land.

In the model above, the feature vector X is a linear combination ofvariables, in this case NIR reflectance, Red reflectance, and thevegetation index TSAVI. The vegetation index TSAVI is computed via thefollowing equation,

$\begin{matrix}{{TSAVI} = \frac{a\left\lbrack {{NIR} - \left( {a*{Re}\mspace{11mu} d} \right) - b} \right\rbrack}{{{Re}\mspace{11mu} d} + \left( {a*{NIR}} \right) - \left( {a*b} \right)}} & \left( {{Equation}\mspace{14mu} 2.0} \right)\end{matrix}$

where a and b are the slope and intercept of the soil line. In anembodiment, the slope and intercept of the soil line are not calculatedfor individual scenes. Instead, a median soil line slope and interceptcan be used. For example, setting a=1.2 and b=0.04 has been shown intesting to produce accurate results.

The coefficients A are then estimated using the maximum likelihoodmethod (MLE), which is a well-known technique for estimating theparameters of a statistical model.

In some embodiments, in order to improve the time and resources requiredto train the classifier, the spectral analysis logic 171 only uses asubset of the pixels from the labeled pixel data. For example, anembodiment might use all water pixels but only a subset of the dry-landpixels when training the classifier, rather than all the labeled pixelsavailable.

At block 520, the spectral analysis logic 171 uses the trainedclassifier to generate a water probability map of the satellite pixeldata received at block 505. In an embodiment, for each pixel of thesatellite image, the spectral analysis logic 171 retrieves the featuresfor that pixel and uses the trained classifier to produce a waterprobability. Using the logistic regression example above, each pixel ofthe satellite image would have the NIR and red bands extracted, theTSAVI computed, and those features would be plugged into the featurevector X to compute the water probability W using the previouslydetermined coefficients A. The collection of water probabilities overthe pixels results in a water probability map that indicates theprobability of a given pixel in the image representing water. Thespectral analysis logic 171 then records the water probability map inthe model data and field data repository 160 for later use by thecoupling logic 173.

6.0 FLOW SIMULATION

FIG. 6 illustrates a process for generating a potential ponding map fora field in block diagram form according to an embodiment. The followingexplanation assumes that the flow simulation logic 172 implements theprocess illustrated in FIG. 6. FIG. 6 illustrates specific blocks thathave been laid out in a particular order. However, in other embodiments,blocks may be added, removed, divided out, merged, or rearrangedcompared to FIG. 6. The techniques described herein are not limited tothe exact blocks in the exact order illustrated in FIG. 6.

At block 605, the flow simulation logic 172 receives precipitation,elevation, and soil property data for the field. In an embodiment, theflow simulation logic 172 receives the precipitation, elevation, andsoil property data from either the model data and field data repository160 or from one or more external data 110 sources. Additional detailsregarding the collection of the precipitation data, elevation data, andsoil property data is described above in Sections 3.2, 3.3, and 3.4respectively.

At block 610, the flow simulation logic 172 begins the simulation byplacing water over each region of the field based on precipitation. Tobegin the simulation, an initial water level is set for each region ofthe agricultural field. In some embodiments, each region is a pixel ofthe satellite image used by the spectral analysis logic 171. However, inother embodiments, pixels may be grouped into larger regions to speed upthe simulation, with some loss of accuracy due to the decreasedresolution. In an embodiment, the initial water level is set based onthe precipitation data. If the precipitation data is available at thesame resolution as the regions, each region can be set to a water levelequal to the last rainfall encountered by that region. However, oftentimes the precipitation data will be at a lower resolution than theregions. Thus, in such cases, an assumption is made that all locationswhich share the same sensor 112 or weather station have the same amountof precipitation.

At block 615, the flow simulation logic 172 shifts water to neighboringregions based on elevation and soil property. During the simulation, thewater placed on each region is assumed to flow at a particular rate toneighboring regions and should be the same when the flow stops. This isperformed iteratively, with block 615 being performed until a stoppingcondition is reached at block 620. During each iteration, an amount ofwater is removed from higher elevation regions and added to lowerelevation neighbors until the total elevation (ground elevation+addedelevation due to the water) is even between the neighbors. The waterflow may be a static amount, or may be dependent on the difference inthe height between the high-elevation pixels and the lower-elevationneighbors. In some cases, a percentage of the water may be moved at eachiteration.

The following pseudo-code provides an example of how the water flows toneighbors during block 615. However, embodiments are not limited to thealgorithm shown in the example pseudo-code. The following pseudo-codeassumes that the water values at each region has already been set atblock 610, region positions are given by [row, col], N represents thenumber of columns, M represents the number of rows, lidar represents amatrix of elevations at each region, and water represents a matrix ofwater quantities at each region. The pseudo-code is broken into twosub-parts, one which controls the horizontal flow of water and one whichcontrols the vertical flow of water. In an embodiment, each iteration ofblock 615 involves executing each sub-part one time. The pseudo-codebelow uses instructions and/or commands similar to R, an open sourcesystem widely used for analysis of large sized data. However,embodiments are not limited to being implemented using R and may beimplemented using any number of programming languages, such as Java,C++, Ruby, Perl, and so forth.

#water flows from left to right (negative value means right to left) forcol =1 to N−1 { elev1 <− lidar.matrix[, col] # returns a vector ofelevation for column col elev2 <− lidar.matrix[,col+1] # returns avector of elevation for column col+1 water1 <− water.matrix[, col]#returns a vector of water amount for col water 2 <− water.matrix[,col+1] #returns a vector of water amount for col +1 total.water <−water1 + water2 diff <− elev1 − elev2 # redistribute / rebalance thewater for the two columns water.level <− pmax(total.water − diff, 0) / 2water.matrix[,col] <− pmin(water.level, total.water) water.matrix[,col+1] <− total.water − water.matrix[,col] } #water flows downside(negative value means upside) for row =1 to M−1 { elevl <−lidar.matrix[row, ] # returns a vector of elevation for row elev2 <−lidar.matrix[row+1,] # returns a vector of elevation for row+1 water1 <−water.matrix[row,] # returns a vector of water amount for row water2 <−water.matrix[row+1, ] #returns a vector of water amount for row +1total.water <− water1 + water2 diff <− elev1 − elev2 # redistribute /rebalance the water for the two rows water.level <− pmax(total.water −diff, 0) / 2 water.matrix[row,] <− pmin(water.level, total.water)water.matrix[row+1, ] <− total.water − water.matrix[row,] }

As water flows a certain amount of water will infiltrate into the soil.The exact amount of infiltrated water depends on many factors, such assoil electrical conductivity, available water capacity, flow duration,flow rate, direction, and so forth, which can be very difficult toaccurately model. However, to determine potential ponding areas, anexact physical model is not required. In an embodiment, the flow processis simplified by using available water capacity as the sole factor,which has been shown through experimentation to have the largest impacton whether water will be ponded. Thus, as water flows, an amount ofwater proportional to the water capacity is removed due to infiltrationand not added to the neighboring region.

With the above assumption, let the proportionality coefficient be α. Foreach region, the amount of water that contributes to ponding is αC(i,j), where C(i, j) is the water capacity for region (i, j). The value ofa may be set based on domain knowledge or through experimentation todetermine a value that appears to produce accurate results.

At block 620, the flow simulation logic 172 determines whether astopping condition for the simulation has been reached. In anembodiment, the stopping condition is to run the simulation for apre-determined number of iterations. However, in other embodiments, thestopping condition for the simulation may be when a convergence point isreached where water has stopped shifting to neighbors or less than athreshold amount of water has shifted during the previous iteration. Thestopping conditions may even be combined, using the number of iterationsas a way to prevent the simulation from taking an excessive amount oftime to converge. For example, the stopping condition may be convergenceor a pre-set number of iterations, whichever occurs first.

At block 625, the flow simulation logic 172 records areas with pooledwater in a potential ponding map. After the stopping condition at block620 has been triggered, regions which still have water on them aremarked in the potential ponding map. For example, the map may be abitmap of the regions or pixels that identifies ponding water using thevalue 1 for water and 0 for dry-land. Thus, the potential ponding mapidentifies the regions where water is likely to have ponded afterrainfall. The potential ponding map is then stored in the model data andfield data repository 160 for later use by the coupling logic 173.

7.0 COUPLING ANALYSIS

FIG. 7 illustrates a process for generating a per-pixel ponding waterclassification of a satellite image in block diagram form according toan embodiment. The following explanation assumes that the coupling logic173 implements the process illustrated in FIG. 7. FIG. 7 illustratesspecific blocks that have been laid out in a particular order. However,in other embodiments, blocks may be added, removed, divided out, merged,or rearranged compared to FIG. 7. The techniques described herein arenot limited to the exact blocks in the exact order illustrated in FIG.7.

The classification of water pixels based on some machine learningtechniques, such as logistic regression, do not consider the couplingeffects between neighboring pixels. For example, for two pixels with thesame NIR band values, the pixel surrounded by water pixels is morelikely to actually represent water than the pixel surrounded by dry-landpixels. The coupling logic 173 models such effects to increase thereliability and accuracy of the probability estimations produced by thespectral analysis logic 171. Furthermore, the spectral analysis logic171 is also reconciled with the results of the flow simulation logic172. Pixels which have a high probability of being water as determinedby the spectral analysis logic 171 and are also in locations whereponding water is likely to occur as determined by the flow simulationlogic 172 are more likely to represent ponding water. Whereas pixels forwhich the spectral analysis logic 171 and the flow simulation logic 172disagree are considered less likely to represent ponding water.

In the following example a Markov Random Field (MRF) is utilized tomodel the aforementioned effects. However, the techniques describedherein are not limited to using MRFs to model the coupling betweenpixels. In the examples below, the MRF model takes as input, the waterprobability map generated by the spectral analysis logic 171, thepotential ponding map produced by the flow simulation logic 172, theband values of each pixel in the satellite image, and a cropland maskidentifying which pixels represent agricultural fields. The examplesbelow also assume that logistic regression was the classificationtechnique utilized by the spectral analysis logic 171 to generate thewater probability map, but this is not required in all embodiments.

At block 705, the coupling logic 173 receives a water probability map, apotential ponding map, and a cropland mask. In an embodiment, thecoupling logic 173 receives the water probability map, potential pondingmap, and cropland mask from the model data and field data repository160. For example, the water probability map may have been previouslyproduced by the spectral analysis logic 171 and stored in the model dataand field data repository 160. Additional details regarding thegeneration of the water probability map are provided above in Sections4.0-4.2. The potential ponding map may have been previously produced bythe flow simulation logic 172 and stored in the model data and fielddata repository 160. Additional details regarding the generation of thepotential ponding map are provided above in Section 5.0. The croplandmask may have been previously received from an external data servercomputer 108 and stored in the model data and field data repository 160.Additional details regarding the cropland mask are provided above inSection 3.5.

At block 710, the coupling logic 173 generates a graph representinginterconnections between neighboring pixels. The graph of the MRF (a setof vertices V and edges E) is constructed as follows. For every twoneighboring pixels (4-neighbor) i and j, there is an edge (i, j)∈E thatconnects node i and node j. Each node represents a random variable Ywhich is 1 if ponding water or 0 if dry-land.

At block 715, the coupling logic 173 sets the probability ofrepresenting ponding water for each pixel in the graph based on thewater probability map, potential ponding map, cropland mask, and theconnections between neighboring pixels.

In an embodiment, for a given pixel p, W_(p), S_(p), and M_(p)(representing water probability map value, potential ponding map value,and cropland mask value for the pixel respectively) are available aswell as the band values for the pixel. The goal is to estimate Y_(p),the class label for the pixel. Unlike logistic regression whichestimates Y_(p) for each individual pixel, the coupling logic 173estimates Y_(p) by maximizing the joint probability for all pixels, i.e.P(Y)=P(Y₁, Y₂, . . . , Y_(N)), where N is the total number of pixels.The joint probability of all random variables Y_(i) is then given by,

$\begin{matrix}{{P(Y)} \propto {\prod\limits_{i \in V}\; {{f\left( Y_{i} \right)}{\prod\limits_{{({i,j})} \in E}{g\left( {Y_{i},Y_{j}} \right)}}}}} & \left( {{Equation}\mspace{14mu} 3.0} \right)\end{matrix}$

where ƒ(Y_(i)) is the potential function for node i and g(Y_(i), Y_(j))is the potential function for the edge between node i and node j. Thefunction ƒ is composed of three components,ƒ(Y_(i))=ƒ_(w)(Y_(i))ƒ_(s)(Y_(i))ƒ_(m)(Y_(i)).

To specify node potential ƒ_(w), the probability map W is utilized. Theclass with higher probability has a higher potential. To avoid zerovalues, let ƒ_(w)(Y_(i))=1+W(Y_(i)). For edge potential g, theassumption can be made that the closer the difference between the NIRvalues of the pixels, the more likely that those pixels will have thesame class labels. Therefore, g can be modeled as a function of the NIRdifference,

$\begin{matrix}{{g\left( {Y_{i},Y_{j}} \right)} = \left\{ {\frac{1 + e^{{{- }{NIR}_{i}} - {{NIR}_{j}{{/D}}}}}{1}\frac{{{if}\mspace{14mu} Y_{i}} = Y_{j}}{{{if}\mspace{14mu} Y_{i}} \neq Y_{j}}} \right.} & \left( {{Equation}\mspace{14mu} 4.0} \right)\end{matrix}$

Where D controls the decreasing rate of the potential value, which canbe set based on experience with the problem domain or by testing valuesto determine one that appears to work best. For example, D may be set to3000 which has shown good performance in testing.

To take the simulation results into account, the ponding map S isutilized. The pixel i is considered a ponding pixel if S_(i)=1 and notponding if S_(i)=0. The water probability from images will bestrengthened if coinciding with the simulation results, and weakenedotherwise. Therefore, ƒ_(s)(Y_(i)=S_(i))=exp(β) andƒ_(s)(Y_(i)≠S_(i))=1, where β represents the importance weight of thesimulation results. For β=0, the results of the simulation areessentially discarded. With increasing β, more importance is placed onthe simulation results. The value for β may be set based on knowledge ofthe problem domain or determined after testing various values todetermine one that works well. For example, β=ln(2) has shown goodresults during testing. For the mask map M, ƒ_(M)(Y_(i)=1)=exp(−inf) andƒ_(M)(Y_(i)=0)=1 for M_(i)=1 (not cropland) and ƒ_(M)(Y_(i))=1 forM_(i)=0 (cropland). Thus, pixels which are not cropland are essentiallyclassified as not being ponding water since those areas are not thefocus of the analysis. However, in other embodiments, the cropland maskmay be omitted if the satellite image is known to only contain pixelsrelated to cropland. Furthermore, the techniques described herein may beapplied to satellite images for non-cropland areas, such as determiningareas where ponding water has accumulated that may be at risk forattracting dangerous insects, such as mosquitos. In such cases, thecropland mask may be omitted if the area being analyzed does notspecifically pertain to cropland.

If F, G, F_(W), F_(S), F_(M) are set to be the negative logarithm formof ƒ, g, ƒ_(w), ƒ_(s), and ƒ_(m) respectively, then

$\begin{matrix}\begin{matrix}{{E(Y)} = {{\sum\limits_{i \in V}{F\left( Y_{i} \right)}} + {\sum\limits_{{({i,j})} \in E}{{G\left( {Y_{i}Y_{j}} \right)}\mspace{304mu} \left( {{Equation}\mspace{14mu} 5.0} \right)}}}} \\{= {{\sum\limits_{i \in V}{F_{W}\left( Y_{i} \right)}} + {\sum\limits_{i \in V}{F_{S}\left( Y_{i} \right)}} + {\sum\limits_{i \in V}{F_{M}\left( Y_{i} \right)}} + {\sum\limits_{{({i,h})} \in E}{G\left( {Y_{i}Y_{j}} \right)}}}}\end{matrix} & \;\end{matrix}$

Thus, the joint probability is,

P(Y)∝exp(−E(Y))  (Equation 6.0)

At block 720, the coupling logic 173 estimates a classification for eachpixel that maximizes the joint probability over the graph. In anembodiment, maximizing the joint probability is equivalent to minimizingthe energy function E(Y). As an example, minimizing the energy functionE(Y) could be performed using Loopy Belief Propagation (LBP), which isan iterative algorithm. After a threshold number of iterations, thealgorithm converges and a suboptimum set of labels will be generated foreach pixel. When N is large, the inference process may be very timeconsuming. However, in some embodiments, to increase the efficiency ofthe inference process each tile considered by LBP can be broken downinto multiple sub-tiles. The end result is a ponding water map thatidentifies, for each pixel of the satellite image of the agriculturalfield, whether that pixel represents ponding water or dry land. Theponding water map is then stored in the model data and field datarepository 160 for later use by the alert logic 174.

8.0 ALERT EXAMPLES

The alert logic 174 represents instructions used by the ponding waterdetection subsystem 170 to generate alerts based on the detection ofponding water by the coupling logic 173.

In some embodiments, when the coupling logic 173 produces the pondingwater map, if the map shows areas of significant ponding water, thecoupling logic 173 invokes the alert logic 174 (e.g. through an API,method/function call, inter-process communication mechanism, and soforth). For example, in order to avoid cases where a very small region,such as a single pixel of ponding water, triggers an alert a thresholdnumber of neighboring pixels classified as ponding water may be used todetermine if the ponding is significant. If the ponding water isdetermined to be significant, the alert logic 174 is invoked. However,in some embodiments, the coupling logic 173 is configured to invoke thealert logic 174 if any ponding water pixels are detected on cropland.

Once invoked, the alert logic 174 retrieves the ponding water mask fromthe model data and field data repository 160 and produces one or morealerts. In some embodiments, the alerts take the form of a message ormessages sent to a device associated with the user 102 which specifythat ponding water has been detected in the field. As one example, themessage may include the coordinates corresponding to the pixels whichhave been identified as containing ponding water. As another example,the message may contain or provide a link to map that displays the fieldor fields where ponding water has been detected and highlights thepixels determined to contain ponding water, such as by displaying themin a particular color. The message may be sent in a myriad of differentways. For example, the message may take the form of an automated phonecall or text message sent to a mobile phone associated with the user102. As another example, the message may take the form of an email sentto an email account associated with the user 102. As yet anotherexample, the field manager computing device 104 may execute anapplication which is configured to interface with the alert logic 174and receive messages for display to the user 102. As yet anotherexample, the agricultural intelligence computer 130 may be configured asa web server or host a component capable of running a web server. Insuch cases, the message could contain a link to a website hosted by theagricultural intelligence computer system 130 which displays the alertand/or location information for the detected ponding water. The exactalert generated and the manner in which the alert is communicated to adevice of the user 102 is not critical.

8.1 Guided Tour of Problem Areas

In an embodiment, the alert logic 174 estimates an shortest path for theuser 102 or an agent of the user 102 to take to visit the areas ofponding water identified by the coupling logic 173. For example, thefield may have multiple areas of ponding water which the user 102 or anagent will need to physically visit to either verify that ponding wateractually exists at that location and/or begin the process of drainingthe ponding water from the field.

Estimating the optimum path to visit all the affected areas can beaccomplished using techniques similar to solutions to the TravelingSalesman Problem (TSP). TSP is a well-known problem in computer sciencewhere, given a list of cities and the distances between each pair ofcities, the goal is to find the shortest route possible that visits eachcity exactly once and returns to the point of origin. In the presentcase, the “cities” are the various locations where ponding water hasbeen detected. However, other aspects of the problem can in someembodiments be modified to better suit the task of traversing anagricultural field. For example, the model data and field datarepository 160 may store information related to the field such as whichcrops are growing in which locations and the current stage of theirgrowth cycle. Some crops may be more vulnerable to ponding waterdepending on type and growth stage. As a result, if the detected pondingwater locations correlate to areas where vulnerable crops are known tobe located, those areas may be prioritized over other areas. Inaddition, affected areas may be prioritized by the amount of waterdetected. For example, the number of interconnected ponding water pixelsmay indicate a severity of the ponding water at that location. Theprioritization may be accomplished by penalizing routes that place theprioritized areas later in the route. For example, the distance metricfor paths leading to the prioritized areas may be given a weight toartificially increase the distance the later the area is placed alongthe route. In addition, straight line paths may not be available betweenthe detected ponding water areas. Thus, provided a map of the roads ortrails to reach areas across the field are known or can be obtained fromexternal data 110, the distance between the affected areas may be set toincorporate the distance of the known roads or trials that connect theponding water areas instead of the straight line distance. There areseveral well-known solutions to the TSP, such as trying all permutationsof routes (also known as brute force search) as well as heuristic andapproximation algorithms (for example, nearest neighbor, pairwiseexchange, Christofides' algorithm, etc.). After a solution has beenobtained, the alert logic 174 sends a message to a device associatedwith the user 102 that includes or links to a map identifying theshortest route. Additionally or alternatively, the route may also begiven in the form of written directions.

8.2 Weather Index Insurance Applications

Weather Index Insurance (WII) is a relatively new insurance system thatprovides a payout based on measurable weather phenomenon, rather thanactual loss to crop yield. In many locations in the world it can bedifficult or expensive for insurance companies to send out an agent toverify crop loss. However, with WIT, the insurance companies insteadidentify weather phenomenon, such as floods, droughts, heat, cold, andso forth that are known to cause loss in crop yield. The farmer and theinsurance company then agree upon the criteria that will be used todetermine the payout and the amount of the payout based on the estimatedcrop loss due to specific weather phenomenon. For example, iftemperatures in excess of 100 degrees are observed for seven days in arow while the crop was in an early growth stage, the insurance policypays out. Since it is often times cheaper and more efficient to detectweather phenomenon, such as by satellite imagery, weather stations, andother sensing technologies, than to send out physical agents to verifyactual crop loss, it becomes far easier for both parties to determinewhen payout of the insurance policy should occur.

In some embodiments, the agricultural intelligence computer system 130includes components which maintain a WII monitoring service or isconfigured to communicate with a WII monitoring service. In someembodiments, in cases where the user 102 has an account with a WIIpolicy based on flooding, the alert logic 174 is configured to send analert message to the WII monitoring service in addition to or instead ofa device of the user 102. For example, the message may include the areaswhich have accumulated ponding water, how long the ponding water hasestimated to be on the field, what crops were planted on those fields,the current growth stage of the crop, and so forth. The aforementionedinformation may be collected from the model data and field datarepository 160 or estimated based on information from the model data andfield data repository 160. For example, time the water was on the fieldmay be determined by using precipitation data to determine when rainfalloccurred and comparing that date to the time at which the ponding wateranalysis was performed. If the WII monitoring service determines thatthe information contained in the message sent by the alert logic 174meets the criteria for a WII policy, the WII monitoring service may flagthe policy for payout.

9.0 ALTERNATIVE APPLICATIONS

In many of the embodiments described above, the spectral analysis logic171, flow simulation logic 172, coupling logic 173, and alert logic 174have been described as detecting ponding water within an agriculturalfield and alerting a user if ponding water is detected. However,detecting ponding water is useful even for geographic areas which do notrepresent agricultural fields. For example, golf courses span largeareas and maintain grasses that are costly to replace and maintain.Thus, the ponding detection techniques described herein may also beemployed to detect ponding water on a golf course and alert an employeeto drain affected areas. As another example, residential or park areasmay be at risk of dangerous insects that are known to breed in pondedwater, such as mosquitos which are known to spread disease. A governmentagency may employ the techniques described herein to detect pondingwater that may act as an incubator for such insects and deploy teams todrain the area to limit the risk of infection. As yet another example,ponding water is also known to pool of residences, such as houses orapartment buildings, which can eventually lead to roof damage. Thus, thesame ponding water detection techniques can be employed on images ofresidential areas to detect ponding water on roofs and alert theresidence of the need to rectify the problem. In cases where thetechniques described herein are applied to non-cropland areas, thecoupling logic 173 may omit the cropland data, since cropland is not thetarget of the detection, and may instead employ other type of masks thatidentify the golf course, park, residential areas, and so forth.

10.0 EXTENSIONS AND ALTERNATIVES

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the disclosure, and what isintended by the applicants to be the scope of the disclosure, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

11.0 ADDITIONAL DISCLOSURE

Aspects of the subject matter described herein are set out in thefollowing numbered clauses:

1. A method comprising: a computer system receiving image data of afield comprising a plurality of pixels, precipitation data for the fieldindicating one or more amounts of rainfall over one or more regions ofthe field, elevation data indicating one or more elevations over the oneor more regions of the field, and soil property data indicating one ormore properties of soil found across the one or more regions of thefield; the computer system using a classifier that has been trained toidentify water pixels to estimate a probability of representing waterfor each pixel of the plurality of pixels; the computer system running aflow simulation over the field based on the precipitation data, theelevation data, and the soil property data to identify a set of pixelswithin the image data that are likely to have ponding water after therainfall; the computer system determining a class for each pixel of theplurality of pixels indicating whether the pixel represents pondingwater based on the probability of representing water for each pixel ofthe plurality of pixels and the set of pixels within the image data thatare likely to have ponding water after the rainfall; the computer systemgenerating an alert based on the determination of the class for eachpixel of the plurality of pixels.

2. The method of Clause 1, wherein the image data is satellite imagedata.

3. The method of any of Clauses 1-2, wherein the classifier is logisticregression that has been trained on labeled pixel data derived from oneor more images of one or more agricultural fields.

4. The method of any of Clauses 1-3, wherein running the flow simulationincludes: initializing a water level for each region of a plurality ofregions of the field; iteratively rebalancing water levels between theplurality of regions, wherein at each iteration water flows from higherelevation regions to neighboring lower elevation regions based on theelevation data until a stopping condition occurs, wherein as water flowsfrom the higher elevation regions to the neighboring lower elevationregions an amount of water is removed by being absorbed by soil based onthe soil property data; after iteratively rebalancing the water levelsbetween the plurality of regions of the field, using regions of theplurality of regions which still contain water to identify the set ofpixels within the image data that are likely to have ponding water afterthe rainfall.

5. The method of any of Clauses 1-4, wherein each pixel of the pluralityof pixels comprises one or more light bands and determining the classfor each pixel of the plurality of pixels includes: generating a model,wherein the model includes a graph comprising a set of vertices, eachvertex in the set of vertices representing a respective pixel of theplurality of pixels, and a set of edges representing connections betweenneighboring pixels of the plurality of pixels; for each vertex in theset of vertices, setting a probability of being ponding water based onthe one or more light bands for the pixel represented by the vertex, theprobability of representing water for the pixel represented by thevertex, whether the pixel represented by the vertex is in the set ofpixels that are likely to have ponding water, and a probability ofneighboring pixels of the pixel represented by the vertex being pondingwater; determining a class for each vertex in the set of vertices thatmaximizes a joint probability over the graph.

6. The method of Clause 5, wherein the model is implemented using aMarkov Random Field.

7. The method of any of Clauses 5-6, wherein the model increases theprobability of being ponding water for a given vertex when theprobability of representing water for the pixel represented by the givenvertex indicates that the pixel represented by the given vertex likelyrepresents water and the pixel represented by the given vertex is in theset of pixels that are likely to have ponding water.

8. The method of any of Clauses 5-7, further comprising receivingcropland data that indicates which pixels of the plurality of pixelscorresponds to cropland, wherein the model classifies the pixelrepresented by a given vertex as not ponding water if the cropland dataindicates that the pixel represented by the given vertex does notcorrespond to cropland.

9. The method of any of Clauses 1-8, wherein generating the alertincludes sending a message to a user device that specifies ponding waterhas been detected on the field and one or more locations where theponding water has been detected based on pixels which have beenclassified as representing ponding water.

10. The method of any of Clauses 1-9, wherein generating the alertincludes identifying a shortest route to visit each area on the fieldcorresponding to pixels which have been classified as representingponding water.

11. One or more non-transitory computer-readable media storinginstructions that, when executed by one or more computing devices,causes performance of any one of the methods recited in Clauses 1-10.

12. A system comprising one or more computing devices comprisingcomponents, implemented at least partially by computing hardware,configured to implement the steps of any one of the methods recited inClauses 1-10.

What is claimed is:
 1. A method comprising: a computer system receivingimage data of a field comprising a plurality of pixels, precipitationdata for the plurality of pixels of the image data of the fieldindicating one or more amounts of rainfall over one or more regions ofthe field, elevation data indicating one or more elevations over the oneor more regions of the field, and soil property data indicating one ormore properties of soil found across the one or more regions of thefield; the computer system running a flow simulation over the pluralityof pixels of the image data of the field by: initializing a water levelfor each region of a plurality of regions of the field; iterativelyrebalancing water levels between the plurality of regions; wherein ateach iteration water flows from higher elevation regions to neighboringlower elevation regions based on the elevation data until a stoppingcondition occurs; wherein as water flows from the higher elevationregions to the neighboring lower elevation regions an amount of water isremoved by being absorbed by soil based on the soil property data; afteriteratively rebalancing the water levels between the plurality ofregions of the field, identifying, using regions of the plurality ofregions which still contain water, a set of pixels within the image datathat are likely to have ponding water after the rainfall.
 2. The methodof claim 1, wherein a region of the plurality of regions of the fieldcorresponds to a portion of a satellite image provided for the field;wherein the initializing a water level for each region of a plurality ofregions of the field is performed based on the precipitation data. 3.The method of claim 1, further comprising: for each pixel, from the setof pixels within the plurality of pixels: estimating, using a classifiertrained to estimate probabilities that pixels represent ponding water, aprobability that a pixel from the set of pixels represents water;assigning the probability to the pixel; determining, based on theprobability assigned to the pixel and particular precipitation data,from the precipitation data, assigned to the pixel, a class, from one ormore classes, to indicate the probability that the pixel representsponding water after the rainfall; generating one or more alertscorresponding to the one or more classes determined for the set ofpixels.
 4. The method of claim 3, wherein the classifier is logisticregression that has been trained on labeled pixel data derived from oneor more images of one or more agricultural fields.
 5. The method ofclaim 4, wherein each pixel of the plurality of pixels comprises one ormore light bands; wherein determining the class for each pixel of theplurality of pixels includes: generating a model, wherein the modelincludes a graph comprising a set of vertices, each vertex in the set ofvertices representing a respective pixel of the plurality of pixels, anda set of edges representing connections between neighboring pixels ofthe plurality of pixels; for each vertex in the set of vertices, settinga probability of being ponding water based on the one or more lightbands for the pixel represented by the vertex, a probability ofrepresenting water for the pixel represented by the vertex, whether thepixel represented by the vertex is in the set of pixels that are likelyto have ponding water, and a probability of neighboring pixels of thepixel represented by the vertex being ponding water; determining theclass for each vertex in the set of vertices that maximizes a jointprobability over the graph.
 6. The method of claim 5, wherein the modelis implemented using a Markov Random Field.
 7. The method of claim 5,wherein the model increases the probability of being ponding water for agiven vertex when the probability of representing water for the pixelrepresented by the given vertex indicates that the pixel represented bythe given vertex likely represents water and the pixel represented bythe given vertex is in the set of pixels that are likely to have pondingwater.
 8. The method of claim 5, further comprising receiving croplanddata that indicates which pixels of the plurality of pixels correspondsto cropland, wherein the model classifies the pixel represented by agiven vertex as not ponding water if the cropland data indicates thatthe pixel represented by the given vertex does not correspond tocropland.
 9. The method of claim 5, wherein generating the one or morealerts includes sending a message to a user device that specifiesponding water has been detected on the field and one or more locationswhere the ponding water has been detected based on pixels which havebeen classified as representing the ponding water.
 10. The method ofclaim 5, wherein generating the one or more alerts includes identifyinga shortest route to visit each area on the field corresponding to pixelswhich have been classified as representing ponding water.
 11. Anon-transitory computer-readable storage medium storing one or moreinstructions which, when executed by one or more processors, cause theone or more processors to perform steps comprising: a computer systemreceiving image data of a field comprising a plurality of pixels,precipitation data for the plurality of pixels of the image data of thefield indicating one or more amounts of rainfall over one or moreregions of the field, elevation data indicating one or more elevationsover the one or more regions of the field, and soil property dataindicating one or more properties of soil found across the one or moreregions of the field; the computer system running a flow simulation overthe plurality of pixels of the image data of the field by: initializinga water level for each region of a plurality of regions of the field;iteratively rebalancing water levels between the plurality of regions;wherein at each iteration water flows from higher elevation regions toneighboring lower elevation regions based on the elevation data until astopping condition occurs; wherein as water flows from the higherelevation regions to the neighboring lower elevation regions an amountof water is removed by being absorbed by soil based on the soil propertydata; after iteratively rebalancing the water levels between theplurality of regions of the field, identifying, using regions of theplurality of regions which still contain water, a set of pixels withinthe image data that are likely to have ponding water after the rainfall.12. The non-transitory computer-readable storage medium of claim 11,wherein a region of the plurality of regions of the field corresponds toa portion of a satellite image provided for the field; wherein theinitializing a water level for each region of a plurality of regions ofthe field is performed based on the precipitation data.
 13. Thenon-transitory computer-readable storage medium of claim 11, storingadditional instructions for: for each pixel, from the set of pixelswithin the plurality of pixels: estimating, using a classifier trainedto estimate probabilities that pixels represent ponding water, aprobability that a pixel from the set of pixels represents water;assigning the probability to the pixel; determining, based on theprobability assigned to the pixel and particular precipitation data,from the precipitation data, assigned to the pixel, a class, from one ormore classes, to indicate the probability that the pixel representsponding water after the rainfall; generating one or more alertscorresponding to the one or more classes determined for the set ofpixels.
 14. The non-transitory computer-readable storage medium of claim13, wherein the classifier is logistic regression that has been trainedon labeled pixel data derived from one or more images of one or moreagricultural fields.
 15. The non-transitory computer-readable storagemedium of claim 14, wherein each pixel of the plurality of pixelscomprises one or more light bands; wherein determining the class foreach pixel of the plurality of pixels includes: generating a model,wherein the model includes a graph comprising a set of vertices, eachvertex in the set of vertices representing a respective pixel of theplurality of pixels, and a set of edges representing connections betweenneighboring pixels of the plurality of pixels; for each vertex in theset of vertices, setting a probability of being ponding water based onthe one or more light bands for the pixel represented by the vertex, aprobability of representing water for the pixel represented by thevertex, whether the pixel represented by the vertex is in the set ofpixels that are likely to have ponding water, and a probability ofneighboring pixels of the pixel represented by the vertex being pondingwater; determining the class for each vertex in the set of vertices thatmaximizes a joint probability over the graph.
 16. The non-transitorycomputer-readable storage medium of claim 15, wherein the model isimplemented using a Markov Random Field.
 17. The non-transitorycomputer-readable storage medium of claim 15, wherein the modelincreases the probability of being ponding water for a given vertex whenthe probability of representing water for the pixel represented by thegiven vertex indicates that the pixel represented by the given vertexlikely represents water and the pixel represented by the given vertex isin the set of pixels that are likely to have ponding water.
 18. Thenon-transitory computer-readable storage medium of claim 15, furthercomprising receiving cropland data that indicates which pixels of theplurality of pixels corresponds to cropland, wherein the modelclassifies the pixel represented by a given vertex as not ponding waterif the cropland data indicates that the pixel represented by the givenvertex does not correspond to cropland.
 19. The non-transitorycomputer-readable storage medium of claim 15, wherein generating the oneor more alerts includes sending a message to a user device thatspecifies ponding water has been detected on the field and one or morelocations where the ponding water has been detected based on pixelswhich have been classified as representing the ponding water.
 20. A dataprocessing system comprising: a memory; one or more processors coupledto the memory; spectral analysis logic stored in the memory, executableby the one or more processors, and configured to cause the one or moreprocessors to: receive image data of a field comprising a plurality ofpixels, precipitation data for the plurality of pixels of the image dataof the field indicating one or more amounts of rainfall over one or moreregions of the field, elevation data indicating one or more elevationsover the one or more regions of the field, and soil property dataindicating one or more properties of soil found across the one or moreregions of the field; flow simulation logic stored in the memory,executable by the one or more processors, and configured to cause theone or more processors to: initializing a water level for each region ofa plurality of regions of the field; iteratively rebalancing waterlevels between the plurality of regions; wherein at each iteration waterflows from higher elevation regions to neighboring lower elevationregions based on the elevation data until a stopping condition occurs;wherein as water flows from the higher elevation regions to theneighboring lower elevation regions an amount of water is removed bybeing absorbed by soil based on the soil property data; afteriteratively rebalancing the water levels between the plurality ofregions of the field, identifying, using regions of the plurality ofregions which still contain water, a set of pixels within the image datathat are likely to have ponding water after the rainfall.